Yet Another Model for Arabic Dialect Identification

Ajinkya Kulkarni, Hanan Aldarmaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we describe a spoken Arabic dialect identification (ADI) model for Arabic that consistently outperforms previously published results on two benchmark datasets: ADI-5 and ADI-17. We explore two architectural variations: ResNet and ECAPA-TDNN, coupled with two types of acoustic features: MFCCs and features exratected from the pre-trained self-supervised model UniSpeech-SAT Large, as well as a fusion of all four variants. We find that individually, ECAPA-TDNN network outperforms ResNet, and models with UniSpeech-SAT features outperform models with MFCCs by a large margin. Furthermore, a fusion of all four variants consistently outperforms individual models. Our best models outperform previously reported results on both datasets, with accuracies of 84.7% and 96.9% on ADI-5 and ADI-17, respectively.

Original languageEnglish
Title of host publicationArabicNLP 2023 - 1st Arabic Natural Language Processing Conference, Porceedings
EditorsHassan Sawaf, Samhaa El-Beltagy, Wajdi Zaghouani, Walid Magdy, Nadi Tomeh, Ibrahim Abu Farha, Nizar Habash, Salam Khalifa, Amr Keleg, Hatem Haddad, Imed Zitouni, Ahmed Abdelali, Khalil Mrini, Rawan Almatham
PublisherAssociation for Computational Linguistics (ACL)
Pages435-440
Number of pages6
ISBN (Electronic)9781959429272
Publication statusPublished - 2023
Event1st Arabic Natural Language Processing Conference, ArabicNLP 2023 - Hybrid, Singapore, Singapore
Duration: Dec 7 2023 → …

Publication series

NameArabicNLP 2023 - 1st Arabic Natural Language Processing Conference, Proceedings

Conference

Conference1st Arabic Natural Language Processing Conference, ArabicNLP 2023
Country/TerritorySingapore
CityHybrid, Singapore
Period12/7/23 → …

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software
  • Linguistics and Language

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